Overview

Dataset statistics

Number of variables28
Number of observations146937
Missing cells0
Missing cells (%)0.0%
Duplicate rows281
Duplicate rows (%)0.2%
Total size in memory31.9 MiB
Average record size in memory228.0 B

Variable types

Numeric17
Categorical11

Warnings

MODALIDADE DESPACHO has constant value "NORMAL" Constant
TIPO DECLARACAO IMPORTACAO has constant value "CONSUMO" Constant
TRANSITO has constant value "0" Constant
Dataset has 281 (0.2%) duplicate rowsDuplicates
RECINTO has a high cardinality: 123 distinct values High cardinality
HORAS_EXIG is highly correlated with QTDE HORAS DESPACHOHigh correlation
QTDE HORAS DESPACHO is highly correlated with HORAS_EXIGHigh correlation
QTDE HORAS DISTRIBUICAO is highly correlated with QTDE HORAS RECEPCAOHigh correlation
QTDE HORAS RECEPCAO is highly correlated with QTDE HORAS DISTRIBUICAOHigh correlation
HORAS_EXIG is highly correlated with QTDE HORAS DISTRIBUICAO and 1 other fieldsHigh correlation
QTDE HORAS DISTRIBUICAO is highly correlated with HORAS_EXIG and 1 other fieldsHigh correlation
QTDE HORAS RECEPCAO is highly correlated with HORAS_EXIG and 1 other fieldsHigh correlation
HORAS_EXIG is highly correlated with QTDE HORAS DISTRIBUICAO and 1 other fieldsHigh correlation
QTDE HORAS DISTRIBUICAO is highly correlated with HORAS_EXIG and 1 other fieldsHigh correlation
QTDE HORAS RECEPCAO is highly correlated with HORAS_EXIG and 1 other fieldsHigh correlation
QTDE LI DEF POS REGISTRO is highly correlated with QTDE LI DECEXHigh correlation
QTDE HORAS DISTRIBUICAO is highly correlated with QTDE HORAS DESPACHO and 1 other fieldsHigh correlation
QTDE LI MCT is highly correlated with QTDE LI DFPCHigh correlation
HORAS_EXIG is highly correlated with QTDE HORAS DESPACHOHigh correlation
QTDE LI DECEX is highly correlated with QTDE LI DEF POS REGISTROHigh correlation
QTDE LI ANP is highly correlated with QTDE LI CNENHigh correlation
QTDE LI DFPC is highly correlated with QTDE LI MCT and 1 other fieldsHigh correlation
QTDE LI INMETRO is highly correlated with QTDE LI DFPCHigh correlation
QTDE LI CNEN is highly correlated with QTDE LI ANPHigh correlation
QTDE HORAS DESPACHO is highly correlated with QTDE HORAS DISTRIBUICAO and 1 other fieldsHigh correlation
QTDE HORAS RECEPCAO is highly correlated with QTDE HORAS DISTRIBUICAOHigh correlation
TIPO DECLARACAO IMPORTACAO is highly correlated with TRANSITO and 8 other fieldsHigh correlation
TRANSITO is highly correlated with TIPO DECLARACAO IMPORTACAO and 8 other fieldsHigh correlation
OEA is highly correlated with TIPO DECLARACAO IMPORTACAO and 2 other fieldsHigh correlation
CANAL is highly correlated with TIPO DECLARACAO IMPORTACAO and 2 other fieldsHigh correlation
DIA SEMANA is highly correlated with TIPO DECLARACAO IMPORTACAO and 2 other fieldsHigh correlation
MODALIDADE DESPACHO is highly correlated with TIPO DECLARACAO IMPORTACAO and 8 other fieldsHigh correlation
TIPO CE is highly correlated with TIPO DECLARACAO IMPORTACAO and 2 other fieldsHigh correlation
QTDE LI CNEN is highly correlated with TIPO DECLARACAO IMPORTACAO and 2 other fieldsHigh correlation
QTDE LI BB is highly correlated with TIPO DECLARACAO IMPORTACAO and 2 other fieldsHigh correlation
UNIDADE is highly correlated with TIPO DECLARACAO IMPORTACAO and 2 other fieldsHigh correlation
HORAS_EXIG is highly skewed (γ1 = 41.0805902) Skewed
QTDE HORAS DESPACHO is highly skewed (γ1 = 35.38608492) Skewed
QTDE HORAS PRESENCA is highly skewed (γ1 = 35.44132498) Skewed
QTDE HORAS DISTRIBUICAO is highly skewed (γ1 = 224.2701601) Skewed
QTDE HORAS RECEPCAO is highly skewed (γ1 = 50.11020786) Skewed
QTDE LI DECEX is highly skewed (γ1 = 27.48725362) Skewed
QTDE LI INMETRO is highly skewed (γ1 = 26.20538091) Skewed
QTDE LI ANVISA is highly skewed (γ1 = 29.47481487) Skewed
QTDE LI IBAMA is highly skewed (γ1 = 37.77644084) Skewed
QTDE LI MCT is highly skewed (γ1 = 42.57275678) Skewed
QTDE LI ANP is highly skewed (γ1 = 32.69117292) Skewed
QTDE LI DPF is highly skewed (γ1 = 39.95062258) Skewed
QTDE LI CNPQ is highly skewed (γ1 = 253.1208078) Skewed
QTDE LI DFPC is highly skewed (γ1 = 47.02800142) Skewed
QTDE LI DEF POS REGISTRO is highly skewed (γ1 = 53.25683433) Skewed
HORAS_EXIG has 144302 (98.2%) zeros Zeros
QTDE HORAS DESPACHO has 18008 (12.3%) zeros Zeros
QTDE HORAS DISTRIBUICAO has 143124 (97.4%) zeros Zeros
QTDE HORAS RECEPCAO has 143124 (97.4%) zeros Zeros
QTDE LI DECEX has 114202 (77.7%) zeros Zeros
QTDE LI INMETRO has 138616 (94.3%) zeros Zeros
QTDE LI ANVISA has 140023 (95.3%) zeros Zeros
QTDE LI IBAMA has 144927 (98.6%) zeros Zeros
QTDE LI MAPA has 135675 (92.3%) zeros Zeros
QTDE LI MCT has 146438 (99.7%) zeros Zeros
QTDE LI ANP has 144485 (98.3%) zeros Zeros
QTDE LI DPF has 146350 (99.6%) zeros Zeros
QTDE LI CNPQ has 146922 (> 99.9%) zeros Zeros
QTDE LI DFPC has 146186 (99.5%) zeros Zeros
QTDE LI DEF POS REGISTRO has 134599 (91.6%) zeros Zeros

Reproduction

Analysis started2021-08-01 19:19:48.606112
Analysis finished2021-08-01 19:21:24.825813
Duration1 minute and 36.22 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

ID DI
Real number (ℝ≥0)

Distinct146654
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.996345672 × 1014
Minimum2.399386122 × 1010
Maximum9.999418577 × 1014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2021-08-01T15:21:25.031813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.399386122 × 1010
5-th percentile4.995541812 × 1013
Q12.494965909 × 1014
median5.005943514 × 1014
Q37.490300733 × 1014
95-th percentile9.488432927 × 1014
Maximum9.999418577 × 1014
Range9.999178638 × 1014
Interquartile range (IQR)4.995334824 × 1014

Descriptive statistics

Standard deviation2.884035634 × 1014
Coefficient of variation (CV)0.5772290036
Kurtosis-1.198242835
Mean4.996345672 × 1014
Median Absolute Deviation (MAD)2.497729609 × 1014
Skewness-0.00246071721
Sum-3.721718983 × 1017
Variance8.317661536 × 1028
MonotonicityNot monotonic
2021-08-01T15:21:25.230318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.304119216 × 10143
 
< 0.1%
9.49848172 × 10133
 
< 0.1%
3.062486832 × 10142
 
< 0.1%
4.905930855 × 10142
 
< 0.1%
5.623123375 × 10142
 
< 0.1%
4.569311011 × 10142
 
< 0.1%
7.066875731 × 10142
 
< 0.1%
6.5249499 × 10142
 
< 0.1%
4.909321838 × 10142
 
< 0.1%
1.413614182 × 10142
 
< 0.1%
Other values (146644)146915
> 99.9%
ValueCountFrequency (%)
2.399386122 × 10101
< 0.1%
2.670689726 × 10101
< 0.1%
3.190666877 × 10101
< 0.1%
3.632923104 × 10101
< 0.1%
3.908906454 × 10101
< 0.1%
5.128484196 × 10101
< 0.1%
5.352412968 × 10101
< 0.1%
5.542927796 × 10101
< 0.1%
5.720819458 × 10101
< 0.1%
6.222515530 × 10101
< 0.1%
ValueCountFrequency (%)
9.999418577 × 10141
< 0.1%
9.99932782 × 10141
< 0.1%
9.999245884 × 10141
< 0.1%
9.999238119 × 10141
< 0.1%
9.999227113 × 10141
< 0.1%
9.998948164 × 10141
< 0.1%
9.998652654 × 10141
< 0.1%
9.998608568 × 10141
< 0.1%
9.998561701 × 10141
< 0.1%
9.998549359 × 10141
< 0.1%

TIPO CE
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
HBL
98355 
BL
48582 

Length

Max length3
Median length3
Mean length2.669368505
Min length2

Characters and Unicode

Total characters392229
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHBL
2nd rowHBL
3rd rowHBL
4th rowHBL
5th rowBL

Common Values

ValueCountFrequency (%)
HBL98355
66.9%
BL48582
33.1%

Length

2021-08-01T15:21:25.526533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-01T15:21:25.615290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
hbl98355
66.9%
bl48582
33.1%

Most occurring characters

ValueCountFrequency (%)
B146937
37.5%
L146937
37.5%
H98355
25.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter392229
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B146937
37.5%
L146937
37.5%
H98355
25.1%

Most occurring scripts

ValueCountFrequency (%)
Latin392229
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B146937
37.5%
L146937
37.5%
H98355
25.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII392229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B146937
37.5%
L146937
37.5%
H98355
25.1%

OEA
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
NAO
141892 
sim
 
5045

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters440811
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNAO
2nd rowNAO
3rd rowNAO
4th rowNAO
5th rowNAO

Common Values

ValueCountFrequency (%)
NAO141892
96.6%
sim5045
 
3.4%

Length

2021-08-01T15:21:25.845676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-01T15:21:25.930451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
nao141892
96.6%
sim5045
 
3.4%

Most occurring characters

ValueCountFrequency (%)
N141892
32.2%
A141892
32.2%
O141892
32.2%
s5045
 
1.1%
i5045
 
1.1%
m5045
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter425676
96.6%
Lowercase Letter15135
 
3.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N141892
33.3%
A141892
33.3%
O141892
33.3%
Lowercase Letter
ValueCountFrequency (%)
s5045
33.3%
i5045
33.3%
m5045
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin440811
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N141892
32.2%
A141892
32.2%
O141892
32.2%
s5045
 
1.1%
i5045
 
1.1%
m5045
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII440811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N141892
32.2%
A141892
32.2%
O141892
32.2%
s5045
 
1.1%
i5045
 
1.1%
m5045
 
1.1%

MODALIDADE DESPACHO
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
NORMAL
146937 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters881622
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORMAL
2nd rowNORMAL
3rd rowNORMAL
4th rowNORMAL
5th rowNORMAL

Common Values

ValueCountFrequency (%)
NORMAL146937
100.0%

Length

2021-08-01T15:21:26.142880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-01T15:21:26.225660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
normal146937
100.0%

Most occurring characters

ValueCountFrequency (%)
N146937
16.7%
O146937
16.7%
R146937
16.7%
M146937
16.7%
A146937
16.7%
L146937
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter881622
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N146937
16.7%
O146937
16.7%
R146937
16.7%
M146937
16.7%
A146937
16.7%
L146937
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin881622
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N146937
16.7%
O146937
16.7%
R146937
16.7%
M146937
16.7%
A146937
16.7%
L146937
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII881622
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N146937
16.7%
O146937
16.7%
R146937
16.7%
M146937
16.7%
A146937
16.7%
L146937
16.7%

TIPO DECLARACAO IMPORTACAO
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
CONSUMO
146937 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1028559
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCONSUMO
2nd rowCONSUMO
3rd rowCONSUMO
4th rowCONSUMO
5th rowCONSUMO

Common Values

ValueCountFrequency (%)
CONSUMO146937
100.0%

Length

2021-08-01T15:21:26.416160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-01T15:21:26.495947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
consumo146937
100.0%

Most occurring characters

ValueCountFrequency (%)
O293874
28.6%
C146937
14.3%
N146937
14.3%
S146937
14.3%
U146937
14.3%
M146937
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1028559
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O293874
28.6%
C146937
14.3%
N146937
14.3%
S146937
14.3%
U146937
14.3%
M146937
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin1028559
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O293874
28.6%
C146937
14.3%
N146937
14.3%
S146937
14.3%
U146937
14.3%
M146937
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1028559
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O293874
28.6%
C146937
14.3%
N146937
14.3%
S146937
14.3%
U146937
14.3%
M146937
14.3%

CANAL
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
VERDE
143124 
VERMELHO
 
3104
AMARELO
 
709

Length

Max length8
Median length5
Mean length5.073024493
Min length5

Characters and Unicode

Total characters745415
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVERDE
2nd rowVERDE
3rd rowVERDE
4th rowVERDE
5th rowVERDE

Common Values

ValueCountFrequency (%)
VERDE143124
97.4%
VERMELHO3104
 
2.1%
AMARELO709
 
0.5%

Length

2021-08-01T15:21:26.724330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-01T15:21:26.813101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
verde143124
97.4%
vermelho3104
 
2.1%
amarelo709
 
0.5%

Most occurring characters

ValueCountFrequency (%)
E293165
39.3%
R146937
19.7%
V146228
19.6%
D143124
19.2%
M3813
 
0.5%
L3813
 
0.5%
O3813
 
0.5%
H3104
 
0.4%
A1418
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter745415
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E293165
39.3%
R146937
19.7%
V146228
19.6%
D143124
19.2%
M3813
 
0.5%
L3813
 
0.5%
O3813
 
0.5%
H3104
 
0.4%
A1418
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin745415
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E293165
39.3%
R146937
19.7%
V146228
19.6%
D143124
19.2%
M3813
 
0.5%
L3813
 
0.5%
O3813
 
0.5%
H3104
 
0.4%
A1418
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII745415
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E293165
39.3%
R146937
19.7%
V146228
19.6%
D143124
19.2%
M3813
 
0.5%
L3813
 
0.5%
O3813
 
0.5%
H3104
 
0.4%
A1418
 
0.2%

HORAS_EXIG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct617
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.451758237
Minimum0
Maximum8276
Zeros144302
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2021-08-01T15:21:26.921847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8276
Range8276
Interquartile range (IQR)0

Descriptive statistics

Standard deviation80.49773322
Coefficient of variation (CV)14.76546276
Kurtosis2721.69521
Mean5.451758237
Median Absolute Deviation (MAD)0
Skewness41.0805902
Sum801065
Variance6479.885054
MonotonicityNot monotonic
2021-08-01T15:21:27.339758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0144302
98.2%
4841
 
< 0.1%
2538
 
< 0.1%
11935
 
< 0.1%
9733
 
< 0.1%
2432
 
< 0.1%
9631
 
< 0.1%
16831
 
< 0.1%
12031
 
< 0.1%
16730
 
< 0.1%
Other values (607)2333
 
1.6%
ValueCountFrequency (%)
0144302
98.2%
115
 
< 0.1%
217
 
< 0.1%
313
 
< 0.1%
419
 
< 0.1%
57
 
< 0.1%
612
 
< 0.1%
75
 
< 0.1%
82
 
< 0.1%
131
 
< 0.1%
ValueCountFrequency (%)
82761
< 0.1%
79191
< 0.1%
71041
< 0.1%
51601
< 0.1%
48901
< 0.1%
45811
< 0.1%
44871
< 0.1%
43681
< 0.1%
41501
< 0.1%
41021
< 0.1%

QTDE HORAS DESPACHO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct55827
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.46764215
Minimum0
Maximum9164.244167
Zeros18008
Zeros (%)12.3%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2021-08-01T15:21:27.475657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.749444444
median8.075555556
Q319.79194444
95-th percentile67.28072222
Maximum9164.244167
Range9164.244167
Interquartile range (IQR)15.0425

Descriptive statistics

Standard deviation98.97582421
Coefficient of variation (CV)4.405260844
Kurtosis2247.159796
Mean22.46764215
Median Absolute Deviation (MAD)8.075555556
Skewness35.38608492
Sum3301327.934
Variance9796.213777
MonotonicityNot monotonic
2021-08-01T15:21:27.608562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
018008
 
12.3%
0.00027777777783353
 
2.3%
0.0005555555556287
 
0.2%
0.0008333333333104
 
0.1%
0.00111111111142
 
< 0.1%
0.00138888888927
 
< 0.1%
0.00166666666718
 
< 0.1%
0.00194444444413
 
< 0.1%
6.20805555613
 
< 0.1%
6.242512
 
< 0.1%
Other values (55817)125060
85.1%
ValueCountFrequency (%)
018008
12.3%
0.00027777777783353
 
2.3%
0.0005555555556287
 
0.2%
0.0008333333333104
 
0.1%
0.00111111111142
 
< 0.1%
0.00138888888927
 
< 0.1%
0.00166666666718
 
< 0.1%
0.00194444444413
 
< 0.1%
0.0022222222227
 
< 0.1%
0.00254
 
< 0.1%
ValueCountFrequency (%)
9164.2441671
< 0.1%
8970.2941671
< 0.1%
8823.4161111
< 0.1%
7131.2622221
< 0.1%
7056.1188891
< 0.1%
6219.03251
< 0.1%
5306.231
< 0.1%
4732.2011111
< 0.1%
4610.0236111
< 0.1%
4512.0036111
< 0.1%

QTDE HORAS PRESENCA
Real number (ℝ≥0)

SKEWED

Distinct7176
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.06728232
Minimum0
Maximum5516.55
Zeros14
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2021-08-01T15:21:27.743237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.266666667
Q112.45
median24.05
Q343.68333333
95-th percentile73.4
Maximum5516.55
Range5516.55
Interquartile range (IQR)31.23333333

Descriptive statistics

Standard deviation45.80945371
Coefficient of variation (CV)1.428541816
Kurtosis2832.011486
Mean32.06728232
Median Absolute Deviation (MAD)14.21666667
Skewness35.44132498
Sum4711870.263
Variance2098.506049
MonotonicityNot monotonic
2021-08-01T15:21:27.870896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.23333333306
 
0.2%
10.86666667292
 
0.2%
14.71666667257
 
0.2%
10.85253
 
0.2%
20.53333333237
 
0.2%
39.48333333227
 
0.2%
12.36666667227
 
0.2%
13.75226
 
0.2%
19.5217
 
0.1%
13.53333333211
 
0.1%
Other values (7166)144484
98.3%
ValueCountFrequency (%)
014
< 0.1%
0.0166666666722
< 0.1%
0.0333333333318
< 0.1%
0.057
 
< 0.1%
0.066666666678
 
< 0.1%
0.083333333335
 
< 0.1%
0.19
< 0.1%
0.11666666677
 
< 0.1%
0.13333333337
 
< 0.1%
0.157
 
< 0.1%
ValueCountFrequency (%)
5516.551
< 0.1%
4007.751
< 0.1%
35752
< 0.1%
3491.6666671
< 0.1%
3174.2666671
< 0.1%
2011.1166671
< 0.1%
2010.1166671
< 0.1%
16121
< 0.1%
1560.5833331
< 0.1%
1552.8666671
< 0.1%

QTDE HORAS DISTRIBUICAO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct3777
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.789040129
Minimum0
Maximum9315.139167
Zeros143124
Zeros (%)97.4%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2021-08-01T15:21:28.015510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9315.139167
Range9315.139167
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.32191179
Coefficient of variation (CV)16.38974516
Kurtosis69558.36049
Mean1.789040129
Median Absolute Deviation (MAD)0
Skewness224.2701601
Sum262876.1894
Variance859.7745111
MonotonicityNot monotonic
2021-08-01T15:21:28.151148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0143124
97.4%
40.08752
 
< 0.1%
90.364722222
 
< 0.1%
17.021111112
 
< 0.1%
40.335833332
 
< 0.1%
5.5680555562
 
< 0.1%
26.793611112
 
< 0.1%
41.372777782
 
< 0.1%
25.324166672
 
< 0.1%
89.353611112
 
< 0.1%
Other values (3767)3795
 
2.6%
ValueCountFrequency (%)
0143124
97.4%
0.83805555561
 
< 0.1%
1.0266666671
 
< 0.1%
1.1127777782
 
< 0.1%
1.2630555561
 
< 0.1%
1.2851
 
< 0.1%
1.3486111111
 
< 0.1%
1.4527777781
 
< 0.1%
1.4811111111
 
< 0.1%
1.4816666671
 
< 0.1%
ValueCountFrequency (%)
9315.1391671
< 0.1%
2085.4316671
< 0.1%
1512.5977781
< 0.1%
1460.6422221
< 0.1%
956.38805561
< 0.1%
736.05361111
< 0.1%
692.33222221
< 0.1%
617.20944441
< 0.1%
573.36527781
< 0.1%
497.18722221
< 0.1%

QTDE HORAS RECEPCAO
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct3775
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.18233053
Minimum-0.0002777777778
Maximum2015.262222
Zeros143124
Zeros (%)97.4%
Negative1
Negative (%)< 0.1%
Memory size2.2 MiB
2021-08-01T15:21:28.292848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.0002777777778
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2015.262222
Range2015.2625
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.92068677
Coefficient of variation (CV)11.77393835
Kurtosis5144.036472
Mean1.18233053
Median Absolute Deviation (MAD)0
Skewness50.11020786
Sum173728.1011
Variance193.7855201
MonotonicityNot monotonic
2021-08-01T15:21:28.435867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0143124
97.4%
22.863888892
 
< 0.1%
5.5652
 
< 0.1%
5.8455555562
 
< 0.1%
17.918611112
 
< 0.1%
6.1711111112
 
< 0.1%
21.364722222
 
< 0.1%
52.653055562
 
< 0.1%
23.505277782
 
< 0.1%
71.888055562
 
< 0.1%
Other values (3765)3795
 
2.6%
ValueCountFrequency (%)
-0.00027777777781
 
< 0.1%
0143124
97.4%
0.042222222221
 
< 0.1%
0.10111111111
 
< 0.1%
0.16111111111
 
< 0.1%
0.18277777781
 
< 0.1%
0.19222222221
 
< 0.1%
0.21
 
< 0.1%
0.21055555561
 
< 0.1%
0.24777777781
 
< 0.1%
ValueCountFrequency (%)
2015.2622221
< 0.1%
1512.5180561
< 0.1%
1444.39751
< 0.1%
939.60277781
< 0.1%
719.87555561
< 0.1%
675.81583331
< 0.1%
616.78166671
< 0.1%
473.22277781
< 0.1%
459.191
< 0.1%
458.48194441
< 0.1%

DIA SEMANA
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
SEGUNDA
38222 
QUARTA
31062 
TERCA
28559 
QUINTA
24807 
SEXTA
23845 
Other values (2)
 
442

Length

Max length7
Median length6
Mean length5.904217454
Min length5

Characters and Unicode

Total characters867548
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSEXTA
2nd rowQUINTA
3rd rowSEGUNDA
4th rowSEXTA
5th rowQUARTA

Common Values

ValueCountFrequency (%)
SEGUNDA38222
26.0%
QUARTA31062
21.1%
TERCA28559
19.4%
QUINTA24807
16.9%
SEXTA23845
16.2%
SABADO334
 
0.2%
DOMINGO108
 
0.1%

Length

2021-08-01T15:21:28.734720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-01T15:21:28.831458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
segunda38222
26.0%
quarta31062
21.1%
terca28559
19.4%
quinta24807
16.9%
sexta23845
16.2%
sabado334
 
0.2%
domingo108
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A178225
20.5%
T108273
12.5%
U94091
10.8%
E90626
10.4%
N63137
 
7.3%
S62401
 
7.2%
R59621
 
6.9%
Q55869
 
6.4%
D38664
 
4.5%
G38330
 
4.4%
Other values (6)78311
9.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter867548
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A178225
20.5%
T108273
12.5%
U94091
10.8%
E90626
10.4%
N63137
 
7.3%
S62401
 
7.2%
R59621
 
6.9%
Q55869
 
6.4%
D38664
 
4.5%
G38330
 
4.4%
Other values (6)78311
9.0%

Most occurring scripts

ValueCountFrequency (%)
Latin867548
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A178225
20.5%
T108273
12.5%
U94091
10.8%
E90626
10.4%
N63137
 
7.3%
S62401
 
7.2%
R59621
 
6.9%
Q55869
 
6.4%
D38664
 
4.5%
G38330
 
4.4%
Other values (6)78311
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII867548
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A178225
20.5%
T108273
12.5%
U94091
10.8%
E90626
10.4%
N63137
 
7.3%
S62401
 
7.2%
R59621
 
6.9%
Q55869
 
6.4%
D38664
 
4.5%
G38330
 
4.4%
Other values (6)78311
9.0%

UNIDADE
Categorical

HIGH CORRELATION

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
817800 - PORTO DE SANTOS
75256 
927800 - ITAJAI
19067 
917800 - PORTO DE PARANAGUA
12963 
927700 - PORTO DE SAO FRANCISCO DO SUL
8802 
717600 - PORTO DO RIO DE JANEIRO
8312 
Other values (25)
22537 

Length

Max length38
Median length24
Mean length24.78111708
Min length15

Characters and Unicode

Total characters3641263
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row927800 - ITAJAI
2nd row817800 - PORTO DE SANTOS
3rd row817800 - PORTO DE SANTOS
4th row817800 - PORTO DE SANTOS
5th row927700 - PORTO DE SAO FRANCISCO DO SUL

Common Values

ValueCountFrequency (%)
817800 - PORTO DE SANTOS75256
51.2%
927800 - ITAJAI19067
 
13.0%
917800 - PORTO DE PARANAGUA12963
 
8.8%
927700 - PORTO DE SAO FRANCISCO DO SUL8802
 
6.0%
717600 - PORTO DO RIO DE JANEIRO8312
 
5.7%
1017700 - PORTO DE RIO GRANDE5170
 
3.5%
727600 - PORTO DE VITORIA4430
 
3.0%
417902 - IRF - PORTO DE SUAPE4115
 
2.8%
517800 - PORTO DE SALVADOR3644
 
2.5%
717800 - PORTO DE ITAGUAI2034
 
1.4%
Other values (20)3144
 
2.1%

Length

2021-08-01T15:21:29.148857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
153913
20.4%
porto125059
16.6%
de124923
16.6%
santos75256
10.0%
81780075256
10.0%
itajai19067
 
2.5%
92780019067
 
2.5%
do17114
 
2.3%
rio13482
 
1.8%
91780012963
 
1.7%
Other values (58)116918
15.5%

Most occurring characters

ValueCountFrequency (%)
606081
16.6%
O392094
 
10.8%
0294887
 
8.1%
T227815
 
6.3%
A223963
 
6.2%
8189128
 
5.2%
R188436
 
5.2%
S185139
 
5.1%
7175600
 
4.8%
-153913
 
4.2%
Other values (28)1004207
27.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1994341
54.8%
Decimal Number886928
24.4%
Space Separator606081
 
16.6%
Dash Punctuation153913
 
4.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O392094
19.7%
T227815
11.4%
A223963
11.2%
R188436
9.4%
S185139
9.3%
D151039
 
7.6%
E145374
 
7.3%
P142143
 
7.1%
N110763
 
5.6%
I85992
 
4.3%
Other values (16)141583
 
7.1%
Decimal Number
ValueCountFrequency (%)
0294887
33.2%
8189128
21.3%
7175600
19.8%
1119782
13.5%
946888
 
5.3%
237587
 
4.2%
612962
 
1.5%
44198
 
0.5%
53979
 
0.4%
31917
 
0.2%
Space Separator
ValueCountFrequency (%)
606081
100.0%
Dash Punctuation
ValueCountFrequency (%)
-153913
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1994341
54.8%
Common1646922
45.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
O392094
19.7%
T227815
11.4%
A223963
11.2%
R188436
9.4%
S185139
9.3%
D151039
 
7.6%
E145374
 
7.3%
P142143
 
7.1%
N110763
 
5.6%
I85992
 
4.3%
Other values (16)141583
 
7.1%
Common
ValueCountFrequency (%)
606081
36.8%
0294887
17.9%
8189128
 
11.5%
7175600
 
10.7%
-153913
 
9.3%
1119782
 
7.3%
946888
 
2.8%
237587
 
2.3%
612962
 
0.8%
44198
 
0.3%
Other values (2)5896
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3640648
> 99.9%
Latin 1 Sup615
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
606081
16.6%
O392094
 
10.8%
0294887
 
8.1%
T227815
 
6.3%
A223963
 
6.2%
8189128
 
5.2%
R188436
 
5.2%
S185139
 
5.1%
7175600
 
4.8%
-153913
 
4.2%
Other values (24)1003592
27.6%
Latin 1 Sup
ValueCountFrequency (%)
É563
91.5%
Ã25
 
4.1%
Í25
 
4.1%
Á2
 
0.3%

RECINTO
Categorical

HIGH CARDINALITY

Distinct123
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
9801303 - TCP - TERMINAL DE CONTEINERES DE PARANAGUA S/A
12258 
8931356 - INST. PORTUARIA PUBLICA - SANTOS BRASIL PARTCIPAÇÕES SA
11230 
8931319 - INST.PORT.MAR.ALF-USO PUBL.CIA.BANDERANTES-PT.DE SANTOS/SP
10321 
9101602 - INST. PORT. FLUVIAL DE USO PRIVAT. MISTO_- PORTONAVE S/A
 
7945
8931359 - BRASIL TERMINAL PORTUÁRIO S/A
 
7765
Other values (118)
97418 

Length

Max length70
Median length64
Mean length57.91469133
Min length24

Characters and Unicode

Total characters8509811
Distinct characters60
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st row9101401 - INST.PORT.MAR.ALF.USO PRIVATIVO MISTO-APM TERMINALS ITAJAÍ S
2nd row8931319 - INST.PORT.MAR.ALF-USO PUBL.CIA.BANDERANTES-PT.DE SANTOS/SP
3rd row8931339 - ECOPORTO SANTOS S.A. (PáTIO 2)
4th row8931319 - INST.PORT.MAR.ALF-USO PUBL.CIA.BANDERANTES-PT.DE SANTOS/SP
5th row9981403 - ITAPOÁ TERMINAIS PORTUÁRIOS S/A

Common Values

ValueCountFrequency (%)
9801303 - TCP - TERMINAL DE CONTEINERES DE PARANAGUA S/A12258
 
8.3%
8931356 - INST. PORTUARIA PUBLICA - SANTOS BRASIL PARTCIPAÇÕES SA11230
 
7.6%
8931319 - INST.PORT.MAR.ALF-USO PUBL.CIA.BANDERANTES-PT.DE SANTOS/SP10321
 
7.0%
9101602 - INST. PORT. FLUVIAL DE USO PRIVAT. MISTO_- PORTONAVE S/A7945
 
5.4%
8931359 - BRASIL TERMINAL PORTUÁRIO S/A7765
 
5.3%
9981403 - ITAPOÁ TERMINAIS PORTUÁRIOS S/A7415
 
5.0%
8933204 - SANTOS BRASIL LOGÍSTICA S/A6855
 
4.7%
8931304 - INSTAL.PORT.MAR.- USO PRIV. - TERMARES TER.MAR.ESPECIALIZADO5646
 
3.8%
8931342 - INST.PORT.MAR.ALF.USO PUBLICO-MARIMEX LTDA.-ARM.XXIV/PVP-SAN5622
 
3.8%
8931339 - ECOPORTO SANTOS S.A. (PáTIO 2)5192
 
3.5%
Other values (113)66688
45.4%

Length

2021-08-01T15:21:29.447780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
201809
 
18.0%
s/a55731
 
5.0%
de43175
 
3.9%
inst.port.mar.alf.uso30626
 
2.7%
brasil29538
 
2.6%
santos26748
 
2.4%
terminal20731
 
1.9%
inst20621
 
1.8%
uso18716
 
1.7%
sa14909
 
1.3%
Other values (446)656416
58.7%

Most occurring characters

ValueCountFrequency (%)
972083
 
11.4%
A727159
 
8.5%
R538806
 
6.3%
S520970
 
6.1%
I507974
 
6.0%
T500734
 
5.9%
O485579
 
5.7%
.377695
 
4.4%
P345754
 
4.1%
E325684
 
3.8%
Other values (50)3207373
37.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter5675461
66.7%
Decimal Number1034042
 
12.2%
Space Separator972083
 
11.4%
Other Punctuation471795
 
5.5%
Dash Punctuation316771
 
3.7%
Lowercase Letter17122
 
0.2%
Connector Punctuation8107
 
0.1%
Open Punctuation7215
 
0.1%
Close Punctuation7215
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A727159
12.8%
R538806
9.5%
S520970
9.2%
I507974
9.0%
T500734
8.8%
O485579
8.6%
P345754
 
6.1%
E325684
 
5.7%
N286349
 
5.0%
L280024
 
4.9%
Other values (23)1156428
20.4%
Decimal Number
ValueCountFrequency (%)
3237062
22.9%
9176315
17.1%
1174252
16.9%
0144200
13.9%
899797
9.7%
266257
 
6.4%
458579
 
5.7%
531704
 
3.1%
630150
 
2.9%
715726
 
1.5%
Lowercase Letter
ValueCountFrequency (%)
á11507
67.2%
ù3573
 
20.9%
í1121
 
6.5%
ú870
 
5.1%
â37
 
0.2%
ç4
 
< 0.1%
é4
 
< 0.1%
ó4
 
< 0.1%
õ2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.377695
80.1%
/92351
 
19.6%
,1749
 
0.4%
Space Separator
ValueCountFrequency (%)
972083
100.0%
Dash Punctuation
ValueCountFrequency (%)
-316771
100.0%
Open Punctuation
ValueCountFrequency (%)
(7215
100.0%
Close Punctuation
ValueCountFrequency (%)
)7215
100.0%
Connector Punctuation
ValueCountFrequency (%)
_8107
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5692583
66.9%
Common2817228
33.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A727159
12.8%
R538806
9.5%
S520970
9.2%
I507974
8.9%
T500734
8.8%
O485579
8.5%
P345754
 
6.1%
E325684
 
5.7%
N286349
 
5.0%
L280024
 
4.9%
Other values (32)1173550
20.6%
Common
ValueCountFrequency (%)
972083
34.5%
.377695
 
13.4%
-316771
 
11.2%
3237062
 
8.4%
9176315
 
6.3%
1174252
 
6.2%
0144200
 
5.1%
899797
 
3.5%
/92351
 
3.3%
266257
 
2.4%
Other values (8)160445
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII8412054
98.9%
Latin 1 Sup97757
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
972083
 
11.6%
A727159
 
8.6%
R538806
 
6.4%
S520970
 
6.2%
I507974
 
6.0%
T500734
 
6.0%
O485579
 
5.8%
.377695
 
4.5%
P345754
 
4.1%
E325684
 
3.9%
Other values (34)3109616
37.0%
Latin 1 Sup
ValueCountFrequency (%)
Á23256
23.8%
Í18974
19.4%
Ç18789
19.2%
á11507
11.8%
Õ11412
11.7%
Ã5743
 
5.9%
ù3573
 
3.7%
É2460
 
2.5%
í1121
 
1.1%
ú870
 
0.9%
Other values (6)52
 
0.1%

QTDE LI DECEX
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct109
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7231874885
Minimum0
Maximum397
Zeros114202
Zeros (%)77.7%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2021-08-01T15:21:29.605863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum397
Range397
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.043238402
Coefficient of variation (CV)5.590857787
Kurtosis1532.8965
Mean0.7231874885
Median Absolute Deviation (MAD)0
Skewness27.48725362
Sum106263
Variance16.34777677
MonotonicityNot monotonic
2021-08-01T15:21:29.757835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0114202
77.7%
118041
 
12.3%
26626
 
4.5%
32039
 
1.4%
41772
 
1.2%
5750
 
0.5%
6666
 
0.5%
7378
 
0.3%
8358
 
0.2%
9239
 
0.2%
Other values (99)1866
 
1.3%
ValueCountFrequency (%)
0114202
77.7%
118041
 
12.3%
26626
 
4.5%
32039
 
1.4%
41772
 
1.2%
5750
 
0.5%
6666
 
0.5%
7378
 
0.3%
8358
 
0.2%
9239
 
0.2%
ValueCountFrequency (%)
3971
< 0.1%
3211
< 0.1%
3051
< 0.1%
2051
< 0.1%
1951
< 0.1%
1901
< 0.1%
1851
< 0.1%
1721
< 0.1%
1671
< 0.1%
1562
< 0.1%

QTDE LI INMETRO
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1455725923
Minimum0
Maximum119
Zeros138616
Zeros (%)94.3%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2021-08-01T15:21:29.918503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum119
Range119
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.164377653
Coefficient of variation (CV)7.998604918
Kurtosis1356.398048
Mean0.1455725923
Median Absolute Deviation (MAD)0
Skewness26.20538091
Sum21390
Variance1.355775319
MonotonicityNot monotonic
2021-08-01T15:21:30.062905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0138616
94.3%
15073
 
3.5%
21329
 
0.9%
3539
 
0.4%
4338
 
0.2%
5222
 
0.2%
6169
 
0.1%
7105
 
0.1%
8102
 
0.1%
973
 
< 0.1%
Other values (43)371
 
0.3%
ValueCountFrequency (%)
0138616
94.3%
15073
 
3.5%
21329
 
0.9%
3539
 
0.4%
4338
 
0.2%
5222
 
0.2%
6169
 
0.1%
7105
 
0.1%
8102
 
0.1%
973
 
< 0.1%
ValueCountFrequency (%)
1191
 
< 0.1%
711
 
< 0.1%
621
 
< 0.1%
581
 
< 0.1%
561
 
< 0.1%
541
 
< 0.1%
491
 
< 0.1%
483
< 0.1%
471
 
< 0.1%
461
 
< 0.1%

QTDE LI ANVISA
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09521087269
Minimum0
Maximum65
Zeros140023
Zeros (%)95.3%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2021-08-01T15:21:30.209904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum65
Range65
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8581705471
Coefficient of variation (CV)9.013367096
Kurtosis1303.201944
Mean0.09521087269
Median Absolute Deviation (MAD)0
Skewness29.47481487
Sum13990
Variance0.7364566879
MonotonicityNot monotonic
2021-08-01T15:21:30.340795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0140023
95.3%
14860
 
3.3%
2993
 
0.7%
3391
 
0.3%
4193
 
0.1%
597
 
0.1%
681
 
0.1%
763
 
< 0.1%
838
 
< 0.1%
932
 
< 0.1%
Other values (35)166
 
0.1%
ValueCountFrequency (%)
0140023
95.3%
14860
 
3.3%
2993
 
0.7%
3391
 
0.3%
4193
 
0.1%
597
 
0.1%
681
 
0.1%
763
 
< 0.1%
838
 
< 0.1%
932
 
< 0.1%
ValueCountFrequency (%)
651
 
< 0.1%
541
 
< 0.1%
511
 
< 0.1%
501
 
< 0.1%
481
 
< 0.1%
452
< 0.1%
441
 
< 0.1%
423
< 0.1%
413
< 0.1%
391
 
< 0.1%

QTDE LI IBAMA
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0210158095
Minimum0
Maximum26
Zeros144927
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2021-08-01T15:21:30.467786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2934965859
Coefficient of variation (CV)13.9655142
Kurtosis2079.00458
Mean0.0210158095
Median Absolute Deviation (MAD)0
Skewness37.77644084
Sum3088
Variance0.08614024596
MonotonicityNot monotonic
2021-08-01T15:21:30.585473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0144927
98.6%
11689
 
1.1%
2151
 
0.1%
347
 
< 0.1%
436
 
< 0.1%
520
 
< 0.1%
713
 
< 0.1%
810
 
< 0.1%
1010
 
< 0.1%
67
 
< 0.1%
Other values (13)27
 
< 0.1%
ValueCountFrequency (%)
0144927
98.6%
11689
 
1.1%
2151
 
0.1%
347
 
< 0.1%
436
 
< 0.1%
520
 
< 0.1%
67
 
< 0.1%
713
 
< 0.1%
810
 
< 0.1%
94
 
< 0.1%
ValueCountFrequency (%)
261
 
< 0.1%
241
 
< 0.1%
201
 
< 0.1%
191
 
< 0.1%
183
< 0.1%
172
< 0.1%
163
< 0.1%
151
 
< 0.1%
141
 
< 0.1%
133
< 0.1%

QTDE LI MAPA
Real number (ℝ≥0)

ZEROS

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09887230582
Minimum0
Maximum21
Zeros135675
Zeros (%)92.3%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2021-08-01T15:21:30.704868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4328168478
Coefficient of variation (CV)4.37753367
Kurtosis232.6844441
Mean0.09887230582
Median Absolute Deviation (MAD)0
Skewness10.42369902
Sum14528
Variance0.1873304237
MonotonicityNot monotonic
2021-08-01T15:21:30.807755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0135675
92.3%
19498
 
6.5%
21029
 
0.7%
3438
 
0.3%
4153
 
0.1%
552
 
< 0.1%
631
 
< 0.1%
718
 
< 0.1%
811
 
< 0.1%
109
 
< 0.1%
Other values (10)23
 
< 0.1%
ValueCountFrequency (%)
0135675
92.3%
19498
 
6.5%
21029
 
0.7%
3438
 
0.3%
4153
 
0.1%
552
 
< 0.1%
631
 
< 0.1%
718
 
< 0.1%
811
 
< 0.1%
96
 
< 0.1%
ValueCountFrequency (%)
211
 
< 0.1%
191
 
< 0.1%
181
 
< 0.1%
161
 
< 0.1%
151
 
< 0.1%
144
< 0.1%
132
 
< 0.1%
125
< 0.1%
111
 
< 0.1%
109
< 0.1%

QTDE LI MCT
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004008520659
Minimum0
Maximum10
Zeros146438
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2021-08-01T15:21:30.913905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0805193143
Coefficient of variation (CV)20.08703987
Kurtosis3691.035943
Mean0.004008520659
Median Absolute Deviation (MAD)0
Skewness42.57275678
Sum589
Variance0.006483359975
MonotonicityNot monotonic
2021-08-01T15:21:31.015826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0146438
99.7%
1442
 
0.3%
242
 
< 0.1%
39
 
< 0.1%
44
 
< 0.1%
102
 
< 0.1%
ValueCountFrequency (%)
0146438
99.7%
1442
 
0.3%
242
 
< 0.1%
39
 
< 0.1%
44
 
< 0.1%
102
 
< 0.1%
ValueCountFrequency (%)
102
 
< 0.1%
44
 
< 0.1%
39
 
< 0.1%
242
 
< 0.1%
1442
 
0.3%
0146438
99.7%

QTDE LI ANP
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02404431831
Minimum0
Maximum22
Zeros144485
Zeros (%)98.3%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2021-08-01T15:21:31.125789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum22
Range22
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2717889719
Coefficient of variation (CV)11.30366719
Kurtosis1754.497834
Mean0.02404431831
Median Absolute Deviation (MAD)0
Skewness32.69117292
Sum3533
Variance0.07386924527
MonotonicityNot monotonic
2021-08-01T15:21:31.230768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0144485
98.3%
12000
 
1.4%
2244
 
0.2%
390
 
0.1%
454
 
< 0.1%
521
 
< 0.1%
614
 
< 0.1%
94
 
< 0.1%
74
 
< 0.1%
133
 
< 0.1%
Other values (11)18
 
< 0.1%
ValueCountFrequency (%)
0144485
98.3%
12000
 
1.4%
2244
 
0.2%
390
 
0.1%
454
 
< 0.1%
521
 
< 0.1%
614
 
< 0.1%
74
 
< 0.1%
81
 
< 0.1%
94
 
< 0.1%
ValueCountFrequency (%)
221
 
< 0.1%
202
< 0.1%
182
< 0.1%
171
 
< 0.1%
162
< 0.1%
153
< 0.1%
141
 
< 0.1%
133
< 0.1%
121
 
< 0.1%
111
 
< 0.1%

QTDE LI DPF
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004512137855
Minimum0
Maximum11
Zeros146350
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2021-08-01T15:21:31.338515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.08299042018
Coefficient of variation (CV)18.39270493
Kurtosis3327.685244
Mean0.004512137855
Median Absolute Deviation (MAD)0
Skewness39.95062258
Sum663
Variance0.006887409842
MonotonicityNot monotonic
2021-08-01T15:21:31.434870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0146350
99.6%
1546
 
0.4%
228
 
< 0.1%
36
 
< 0.1%
52
 
< 0.1%
42
 
< 0.1%
111
 
< 0.1%
81
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
0146350
99.6%
1546
 
0.4%
228
 
< 0.1%
36
 
< 0.1%
42
 
< 0.1%
52
 
< 0.1%
61
 
< 0.1%
81
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
111
 
< 0.1%
81
 
< 0.1%
61
 
< 0.1%
52
 
< 0.1%
42
 
< 0.1%
36
 
< 0.1%
228
 
< 0.1%
1546
 
0.4%
0146350
99.6%

QTDE LI CNEN
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
0.0
146871 
1.0
 
49
2.0
 
13
3.0
 
3
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters440811
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0146871
> 99.9%
1.049
 
< 0.1%
2.013
 
< 0.1%
3.03
 
< 0.1%
4.01
 
< 0.1%

Length

2021-08-01T15:21:31.689861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-01T15:21:31.769883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0146871
> 99.9%
1.049
 
< 0.1%
2.013
 
< 0.1%
3.03
 
< 0.1%
4.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0293808
66.7%
.146937
33.3%
149
 
< 0.1%
213
 
< 0.1%
33
 
< 0.1%
41
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number293874
66.7%
Other Punctuation146937
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0293808
> 99.9%
149
 
< 0.1%
213
 
< 0.1%
33
 
< 0.1%
41
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.146937
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common440811
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0293808
66.7%
.146937
33.3%
149
 
< 0.1%
213
 
< 0.1%
33
 
< 0.1%
41
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII440811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0293808
66.7%
.146937
33.3%
149
 
< 0.1%
213
 
< 0.1%
33
 
< 0.1%
41
 
< 0.1%

QTDE LI CNPQ
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0003266706139
Minimum0
Maximum17
Zeros146922
Zeros (%)> 99.9%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2021-08-01T15:21:31.868843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.05925897641
Coefficient of variation (CV)181.4028378
Kurtosis67446.58463
Mean0.0003266706139
Median Absolute Deviation (MAD)0
Skewness253.1208078
Sum48
Variance0.003511626285
MonotonicityNot monotonic
2021-08-01T15:21:31.962804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0146922
> 99.9%
111
 
< 0.1%
141
 
< 0.1%
41
 
< 0.1%
171
 
< 0.1%
21
 
< 0.1%
ValueCountFrequency (%)
0146922
> 99.9%
111
 
< 0.1%
21
 
< 0.1%
41
 
< 0.1%
141
 
< 0.1%
171
 
< 0.1%
ValueCountFrequency (%)
171
 
< 0.1%
141
 
< 0.1%
41
 
< 0.1%
21
 
< 0.1%
111
 
< 0.1%
0146922
> 99.9%

QTDE LI DFPC
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00773120453
Minimum0
Maximum20
Zeros146186
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2021-08-01T15:21:32.076761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1468628345
Coefficient of variation (CV)18.99611295
Kurtosis4042.113314
Mean0.00773120453
Median Absolute Deviation (MAD)0
Skewness47.02800142
Sum1136
Variance0.02156869216
MonotonicityNot monotonic
2021-08-01T15:21:32.185889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0146186
99.5%
1559
 
0.4%
2116
 
0.1%
340
 
< 0.1%
512
 
< 0.1%
410
 
< 0.1%
66
 
< 0.1%
103
 
< 0.1%
71
 
< 0.1%
151
 
< 0.1%
Other values (3)3
 
< 0.1%
ValueCountFrequency (%)
0146186
99.5%
1559
 
0.4%
2116
 
0.1%
340
 
< 0.1%
410
 
< 0.1%
512
 
< 0.1%
66
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
201
 
< 0.1%
151
 
< 0.1%
103
 
< 0.1%
91
 
< 0.1%
81
 
< 0.1%
71
 
< 0.1%
66
 
< 0.1%
512
 
< 0.1%
410
 
< 0.1%
340
< 0.1%

QTDE LI BB
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
0.0
146909 
1.0
 
24
2.0
 
3
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters440811
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0146909
> 99.9%
1.024
 
< 0.1%
2.03
 
< 0.1%
5.01
 
< 0.1%

Length

2021-08-01T15:21:32.448816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-01T15:21:32.527801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0146909
> 99.9%
1.024
 
< 0.1%
2.03
 
< 0.1%
5.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0293846
66.7%
.146937
33.3%
124
 
< 0.1%
23
 
< 0.1%
51
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number293874
66.7%
Other Punctuation146937
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0293846
> 99.9%
124
 
< 0.1%
23
 
< 0.1%
51
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.146937
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common440811
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0293846
66.7%
.146937
33.3%
124
 
< 0.1%
23
 
< 0.1%
51
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII440811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0293846
66.7%
.146937
33.3%
124
 
< 0.1%
23
 
< 0.1%
51
 
< 0.1%

QTDE LI DEF POS REGISTRO
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct78
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2037539898
Minimum0
Maximum312
Zeros134599
Zeros (%)91.6%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2021-08-01T15:21:32.638856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum312
Range312
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.907656815
Coefficient of variation (CV)9.3625495
Kurtosis5907.312812
Mean0.2037539898
Median Absolute Deviation (MAD)0
Skewness53.25683433
Sum29939
Variance3.639154525
MonotonicityNot monotonic
2021-08-01T15:21:32.772932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0134599
91.6%
18571
 
5.8%
21614
 
1.1%
3701
 
0.5%
4396
 
0.3%
5209
 
0.1%
6157
 
0.1%
7101
 
0.1%
884
 
0.1%
1062
 
< 0.1%
Other values (68)443
 
0.3%
ValueCountFrequency (%)
0134599
91.6%
18571
 
5.8%
21614
 
1.1%
3701
 
0.5%
4396
 
0.3%
5209
 
0.1%
6157
 
0.1%
7101
 
0.1%
884
 
0.1%
956
 
< 0.1%
ValueCountFrequency (%)
3121
< 0.1%
1441
< 0.1%
1361
< 0.1%
1211
< 0.1%
1181
< 0.1%
1071
< 0.1%
801
< 0.1%
791
< 0.1%
781
< 0.1%
761
< 0.1%

TRANSITO
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
0
146937 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters146937
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0146937
100.0%

Length

2021-08-01T15:21:33.028471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-01T15:21:33.104902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0146937
100.0%

Most occurring characters

ValueCountFrequency (%)
0146937
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number146937
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0146937
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common146937
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0146937
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII146937
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0146937
100.0%

Interactions

2021-08-01T15:20:24.097675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:24.306460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:24.495960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:24.685525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:24.887731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:25.180553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:25.380869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:25.581317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:25.773361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:25.961448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:26.201298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:26.384400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:26.622434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:26.796451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:27.089379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:27.305800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:27.527211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:27.773588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:27.960056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:28.179472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:28.356033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:28.527547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:28.704775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:28.947730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:29.152856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:29.343978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:29.518085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:29.727531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:29.908050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:30.078144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:30.245667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:30.420200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:30.603711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:30.790213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:31.027580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:31.203112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:31.403578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:31.582100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:31.758631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:31.935707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:32.126591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:32.322594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:32.501629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:32.754628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:32.922751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:33.091414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:33.298891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:33.481436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:33.679875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:33.898292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:34.265845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:34.450544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:34.632783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:34.906091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:35.080627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:35.326127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:35.531471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:35.812068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:35.998857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:36.183962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:36.389612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:36.572294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:36.761753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:37.104375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:37.326553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:37.499512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:37.688063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:37.882632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:38.110969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:38.301457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:38.523863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:38.699393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:38.898891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:39.115639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:39.343885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:39.527930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:39.723963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:39.978780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:40.173254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:40.359756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:40.546259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:40.731766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:41.065045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:41.252727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:41.449196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:41.696332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:41.905805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:42.156743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:42.384399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:42.582829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:42.826180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:43.213148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:43.502843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:43.695988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:43.896678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:44.093727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:44.291031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:44.540812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:44.744093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:44.961514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:45.255510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:45.513322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:45.718635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:45.917160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:46.121579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:46.324138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:46.521580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:46.746017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:47.177865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:47.433182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:47.623922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:47.812806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:47.996278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:48.173944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:48.353465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:48.528000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:48.707716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:48.897515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:49.089150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:49.326062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:49.509739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:49.685857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:49.871361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:50.061855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:50.260931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:50.477358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:50.695926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:50.941822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:51.163300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:51.374364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:51.653586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:51.918882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:52.149267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:52.381727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:52.610512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:52.857378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:53.085878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:53.299308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:53.512740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:53.735179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:53.947135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:54.161597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:54.385067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:54.602569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:54.810397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:55.013626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:55.216742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:55.424490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:55.622366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:55.819648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:56.004732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:56.192215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:56.379748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:56.568232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:56.733787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:56.899312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:57.069892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:57.253836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:57.431052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:57.615973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:57.798259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:57.971393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:58.139410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:58.506474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:58.664995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:58.857074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:59.038614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:59.209126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:59.389679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:59.569166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:59.755702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:20:59.927253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:00.095762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:00.261357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:00.447619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:00.659150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:00.852664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:01.047113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:01.318424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:01.523884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:01.705390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:01.872296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:02.043861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:02.206832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:02.371967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:02.548201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:02.735332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:02.918951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:03.094704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:03.270274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:03.448760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:03.618816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:03.787511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:03.974831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:04.183243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:04.372770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:04.557245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:04.736765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:04.916286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:05.100796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:05.281314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:05.462828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:05.650363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:05.836195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:06.019120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:06.183830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:06.347330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:06.508863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:06.680504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:06.850272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:07.033064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:07.210802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:07.376582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:07.542580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:07.705561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:07.863552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:08.023545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:08.183547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:08.346147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:08.515663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:08.694223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:08.869716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:09.036273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:09.209810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:09.383347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:09.561872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:09.776305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:09.973539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:10.165534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:10.348049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:10.528567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:10.701107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:10.866664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:11.272623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:11.430161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:11.594586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:11.773861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:11.955376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:12.130909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:12.313422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:12.490949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:12.668475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:12.847033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:13.030509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:13.224992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:13.417477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:13.600988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:13.784021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:13.964120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:14.136333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:14.358052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:14.540359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:14.720961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:14.915399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:15.107613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:15.305680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:15.495647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:15.686162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:15.914553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:16.126021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:16.326453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:16.595735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:16.850581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:17.070643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:17.309834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:17.530244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:17.724768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:17.920238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:18.107703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:18.299263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:18.492820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:18.693655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:18.892476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:19.073992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:19.253513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:19.428080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:19.610626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:19.789150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:19.977647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:20.165148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:20.344180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:20.521785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:20.694469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:20.862644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:21.037418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:21.204537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:21.377583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:21.562091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-01T15:21:21.746631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-08-01T15:21:33.191601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-01T15:21:33.526707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-01T15:21:33.876773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-01T15:21:34.241164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-01T15:21:34.565142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-01T15:21:22.095702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-01T15:21:23.879987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

ID DITIPO CEOEAMODALIDADE DESPACHOTIPO DECLARACAO IMPORTACAOCANALHORAS_EXIGQTDE HORAS DESPACHOQTDE HORAS PRESENCAQTDE HORAS DISTRIBUICAOQTDE HORAS RECEPCAODIA SEMANAUNIDADERECINTOQTDE LI DECEXQTDE LI INMETROQTDE LI ANVISAQTDE LI IBAMAQTDE LI MAPAQTDE LI MCTQTDE LI ANPQTDE LI DPFQTDE LI CNENQTDE LI CNPQQTDE LI DFPCQTDE LI BBQTDE LI DEF POS REGISTROTRANSITO
0100010123323450HBLNAONORMALCONSUMOVERDE0.06.19944412.2000000.00.0SEXTA927800 - ITAJAI9101401 - INST.PORT.MAR.ALF.USO PRIVATIVO MISTO-APM TERMINALS ITAJAÍ S0.00.00.00.00.00.00.00.00.00.00.00.00.00
110001863024225HBLNAONORMALCONSUMOVERDE0.021.353056241.6166670.00.0QUINTA817800 - PORTO DE SANTOS8931319 - INST.PORT.MAR.ALF-USO PUBL.CIA.BANDERANTES-PT.DE SANTOS/SP0.00.00.00.00.00.00.00.00.00.00.00.00.00
2100031430658855HBLNAONORMALCONSUMOVERDE0.08.81111140.1500000.00.0SEGUNDA817800 - PORTO DE SANTOS8931339 - ECOPORTO SANTOS S.A. (PáTIO 2)0.00.00.00.00.00.00.00.00.00.00.00.00.00
3100031604383988HBLNAONORMALCONSUMOVERDE0.05.20138934.1833330.00.0SEXTA817800 - PORTO DE SANTOS8931319 - INST.PORT.MAR.ALF-USO PUBL.CIA.BANDERANTES-PT.DE SANTOS/SP0.00.00.00.00.00.00.00.00.00.00.00.00.00
4100036615433154BLNAONORMALCONSUMOVERDE0.00.0000008.9000000.00.0QUARTA927700 - PORTO DE SAO FRANCISCO DO SUL9981403 - ITAPOÁ TERMINAIS PORTUÁRIOS S/A0.01.00.00.00.00.00.00.00.00.00.00.00.00
5100039399587186HBLNAONORMALCONSUMOVERDE0.021.50055645.5833330.00.0SEGUNDA817800 - PORTO DE SANTOS8931319 - INST.PORT.MAR.ALF-USO PUBL.CIA.BANDERANTES-PT.DE SANTOS/SP0.00.00.00.00.00.00.00.00.00.00.00.00.00
6100044274740765HBLNAONORMALCONSUMOVERDE0.07.77027861.3166670.00.0SEXTA817800 - PORTO DE SANTOS8931342 - INST.PORT.MAR.ALF.USO PUBLICO-MARIMEX LTDA.-ARM.XXIV/PVP-SAN0.00.00.00.00.00.00.00.00.00.00.00.00.00
7100046369420239HBLNAONORMALCONSUMOVERDE0.03.45944420.5333330.00.0SEXTA717600 - PORTO DO RIO DE JANEIRO7921303 - INST.PORT.MAR.ALF.USO PUBL.CONS.MULT RIO-T.II-PORTO RJ0.00.01.00.00.00.00.00.00.00.00.00.00.00
810006175524431HBLNAONORMALCONSUMOVERDE0.06.20611145.5666670.00.0QUINTA817800 - PORTO DE SANTOS8931304 - INSTAL.PORT.MAR.- USO PRIV. - TERMARES TER.MAR.ESPECIALIZADO0.00.00.00.00.00.00.00.00.00.00.00.00.00
9100061895025942BLNAONORMALCONSUMOVERDE0.019.8786114.9333330.00.0TERCA927700 - PORTO DE SAO FRANCISCO DO SUL9981403 - ITAPOÁ TERMINAIS PORTUÁRIOS S/A0.00.00.00.00.00.00.00.00.00.00.00.00.00

Last rows

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Duplicate rows

Most frequently occurring

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